Analysis of individual differences in multidimensional scaling via an n-way generalization of “Eckart-Young” decomposition

By J. Carroll, J. Chang. 1970

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Citations in the corpus

Federated Tensor Factorization for Computational Phenotyping (2017, background): “123-4567-24-567/08/06. . . $15.00 DOI: 10.475/123 4 But meaningful use of EHRs is also accompanied with many challenges, for example, diversity of populations, heterogeneous of information, and data sparseness. e large degree of missing and erroneous records also complicates the interpretation and…”
Compression of Deep Convolutional Neural Networks for Fast and Low Power Mobile Applications (2015, methodology): “Two of the most popular tensor decomposition models are CANDECOMP/PARAFAC model (Carroll & Chang, 1970; Harshman & Lundy, 1994; Shashua & Hazan, 2005) and Tucker model (Tucker, 1966; De Lathauwer et al., 2000; Kim & Choi, 2007).”
A Unified Convergence Analysis of Block Successive Minimization Methods for Nonsmooth Optimization (2012, neutral): “The ALS algorithm proposed in [6], [13] is in essence a BCD method.”
Orthogonal Tensor Decompositions (2000, methodology): “The problem of decomposing tensors (also called n-way arrays or multidimensional arrays) is approached in a variety of ways by extending the SVD, principal components analysis (PCA), and other methods to higher orders; see, e.g., [1] [3] [9] [10] [11] [12] [13] [14] [15].”
Decompositions of a Higher-Order Tensor in Block Terms - Part II: Definitions and Uniqueness (2008, methodology): “The two main tensor generalizations of the matrix singular value decomposition (SVD) are, on one hand, the Tucker decomposition/higher-order singular value decomposition (HOSVD) [59, 60, 12, 13, 15] and, on the other hand, the canonical/parallel factor (CANDECOMP/PARAFAC) decomposition [7, 26].”
An Adaptive Algebraic Multigrid Algorithm for Low-Rank Canonical Tensor Decomposition (2011, unknown): “(cid:75) We refer to the canonical decomposition as CANDECOMP/PARAFAC (CP) after the names originally given to it in early papers on the subject [12, 13].”
Low Complexity Damped Gauss-Newton Algorithms for CANDECOMP/PARAFAC (2012, methodology): “,N) [4, 10, 12] such that Y ≈ R ∑”
Algorithm 941 (2014, background): “This can be improved by considering the more general CP (Canonical Polyadic) decomposition [24, 10]”
Higher-order Web link analysis using multilinear algebra (2005, methodology): “Our contribution is the use of a PARAFAC decomposition [22] (also known as the Canonical Decomposition or CANDECOMP decomposition [7]) on a three-way tensor representing the web graph with anchor-text-labeled edges.”